class_docstr: Add random noise to the Thin Plate Spline algorithm.
  
  .. image:: _static/img/RandomThinPlateSpline.png
  
  Args:
      scale: the scale factor to apply to the destination points.
      align_corners: Interpolation flag used by ``grid_sample``.
      mode: Interpolation mode used by `grid_sample`. Either 'bilinear' or 'nearest'.
      same_on_batch: apply the same transformation across the batch.
      p: probability of applying the transformation.
      keepdim: whether to keep the output shape the same as input (True) or broadcast it
               to the batch form (False).
  .. note::
      This function internally uses :func:`kornia.geometry.transform.warp_image_tps`.
  
  Examples:
      >>> img = torch.ones(1, 1, 2, 2)
      >>> out = RandomThinPlateSpline()(img)
      >>> out.shape
      torch.Size([1, 1, 2, 2])
  
  To apply the exact augmenation again, you may take the advantage of the previous parameter state:
      >>> input = torch.randn(1, 3, 32, 32)
      >>> aug = RandomThinPlateSpline(p=1.)
      >>> (aug(input) == aug(input, params=aug._params)).all()
      tensor(True)
code: def generate_parameters(
      self, shape: Tuple[int, ...]
  ) -> Dict[str, Tensor]:
      B, _, _, _ = shape
      src = tensor(
          [
              [
                  [-1.0, -1.0],
                  [-1.0, 1.0],
                  [1.0, -1.0],
                  [1.0, 1.0],
                  [0.0, 0.0],
              ]
          ]
      ).expand(
          B, 5, 2
      )  # Bx5x2
      dst = src + self.dist.rsample(src.shape)
      return {"src": src, "dst": dst}
  
file_path: kornia/kornia/augmentation/_2d/geometric/thin_plate_spline.py
imports: from typing import Any, Dict, Optional, Tuple
  import torch
  from kornia.augmentation._2d.base import AugmentationBase2D
  from kornia.core import Tensor, tensor
  from kornia.geometry.transform import get_tps_transform, warp_image_tps
label_desc: Use image node_id 2 for calling the RandomThinPlateSpline obj's underlying member method code with example usage: obj.generate_parameters(shape) and returns: dict